Leveraging AI in mobile learning to support education: A taxonomy of AI applications

Muhammad Modi Lakulu, Ayad Shihan Izkair, Mohd Fadhil Harfiez Abdul Muttalib, Nur Azlan Zainuddin

Article ID: 7347
Vol 8, Issue 16, 2024

VIEWS - 1367 (Abstract)

Abstract


This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.


Keywords


artificial inelegance; AI; mobile learning; M-Learning; taxonomy; education

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